Automated Crop Disease Detection System using Computer Vision Test phase, srs, design phase and source code final deliverable

/ Category

Deep Learning / Computer Vision

Abstract / Introduction

The objective of this project is to create an automated crop disease detection system that leverages computer vision techniques and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), to accurately identify and classify various crop diseases. You can see the reference paper for better understanding (link is given below). Early detection and diagnosis of crop diseases are crucial for farmers to implement timely treatments, minimize crop loss, and maintain agricultural productivity. You can use the PlantVillage dataset, which is available on the
following links:
	https://github.com/spMohanty/PlantVillage-Dataset
	https://www.kaggle.com/datasets/emmarex/plantdisease
Reference paper link:
https://www.hindawi.com/journals/cin/2019/9142753/
This dataset contains labeled images of healthy and diseased crop leaves, suitable for training and
testing your model.

Functional Requirements:

and compatibility

Image preprocessing: The system should be able to crop and resize input images for optimal processing.
Disease detection: The system must be capable of implementing any CNN model to identify and classify the crop diseases.
User interface: The system must have a user-friendly (desktop-based or android based) interface for uploading the test images and displaying the results (disease type) after classification.
Performance evaluation: The performance evaluation of this application will be based on assessing the effectiveness of your Automated Crop Disease Detection System. You should submit the results based on the accuracy, precision, recall, and F1-score of your model.

Tools:

Supervisor:

Name: Zaid Ismail